{"id":6429,"date":"2020-02-28T15:24:45","date_gmt":"2020-02-28T14:24:45","guid":{"rendered":"https:\/\/quantpedia.com\/?p=6429"},"modified":"2025-06-04T14:01:30","modified_gmt":"2025-06-04T12:01:30","slug":"quantpedia-premium-update-28th-february-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-28th-february-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 28th February 2020"},"content":{"rendered":"

New strategies:<\/strong><\/p>\n\n\n\n

#474 \u2013 Return Cross-Predictability in Firms with Similar Employee Satisfaction<\/a> <\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2010-2018
Indicative performance:<\/strong> 12.95%
Estimated volatility:<\/strong> 15.11%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Xueying Bian et al.: Return Cross-Predictability in Firms with Similar Employee Satisfaction <\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3469633 <\/a>
Abstracto:
We study the return predictability of similar employee satisfaction (SES) firms using new firm-ranking data of employee satisfaction from Glassdoor. We find that the returns of firm peers with SES have a predictive power for focal firm returns. A long-short portfolio sorted on the lagged returns of SES firm peers yields a significant Fama and French (2018) six-factor alpha of 135 bps per month. This result is distinct from industry and inter-firm momentum effects and cannot be explained by risk-based arguments. Our tests suggest that investors\u2019 limited attention is the primary reason of firms\u2019 underreaction to their SES firm returns.<\/p>\n\n\n\n

#47<\/strong>5 – Offshore Sales Networks<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1998-2018
Indicative performance:<\/strong> 8.81%
Estimated volatility:<\/strong> 23.03%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Bai, John (Jianqiu) and Garg, Priya and Wan, Chi: Offshore Sales Networks and Stock Return Predictability<\/strong>
https:\/\/ssrn.com\/abstract=3455426<\/a>
Abstracto:
Based on 10-K textual analysis, we assemble firm-level offshore sales networks (OSN) and find strong return predictability among industry participants that have overlapping offshore sales activities. This intra-industry return predictability based on offshore sales networks is distinct from that along several previously documented economic linkages (e.g., industry momentum, technological links, and standalone vs conglomerate firms). A long-short strategy that exploits the similarity of offshore sales networks yields a monthly alpha of 1.1 percentage points. Moreover, we find that the effect is stronger for firms that receive low investor attention, issue hard-to-read 10-Ks, and pose high arbitrage costs. Our results highlight important asset pricing implications of the commonality of corporate offshore activities, and are broadly consistent with sluggish price adjustment caused by investors\u2019 inattention to offshore networks. <\/p>\n\n\n\n

New research papers related to existing strategies:<\/strong><\/p>\n\n\n\n

#26 – Value (Book-to-Market) Factor<\/a><\/strong><\/p>\n\n\n\n

Fama, French: The Value Premium
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3525096
Abstract
Value premiums, which we define as value portfolio returns in excess of market portfolio returns, are on average much lower in the second half of the July 1963-June 2019 period. But the high volatility of monthly premiums prevents us from rejecting the hypothesis that expected premiums are the same in both halves of the sample. Regressions that forecast value premiums with book-to-market ratios in excess of market (BM-BM_M) produce more reliable evidence of second-half declines in expected value premiums, but only if we assume the regression coefficients are constant during the sample period. <\/p>\n\n\n\n

#269 \u2013 Intraday Currency Seasonality<\/a><\/strong><\/p>\n\n\n\n

Krohn, Mueller, Whelan: Foreign Exchange Fixings and Returns Around the Clock
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3521370
Abstract
This paper documents a new stylised fact in foreign exchange markets: intraday currency returns display prolonged reversals around the major benchmark fixings, characterised by an appreciation of the U.S. dollar pre-fixing and a depreciation thereafter. Tracing returns around the clock, the major fixing during Asian trading hours (Tokyo) and two major fixings during European and U.S. hours (Frankfurt and London) generate a distinct `W\u2019 shaped return pattern over the 24-hour trading day. On either side of the reversal, price drifts persist for hours; moreover, they are a systematic feature of the data being present every day of the week, month of the year, and during each of the 20 years in our sample. We argue these findings require two ingredients (i) a structural demand for dollar immediacy at local currency fixing times; and (ii) pre-fix hedging risk management practices by financial intermediaries. Consistent with this conjecture, we show our findings are unique to the U.S dollar numeraire, amplified in states of high anticipated volatility, low liquidity, and that arbitrageurs can exploit these patterns after taking transaction costs into account. <\/p>\n\n\n\n

And two interesting free blog post has been published during last 2 weeks:<\/strong><\/p>\n\n\n\n

Hierarchical Risk Parity<\/a><\/strong><\/p>\n\n\n\n

Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets\u2019 risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets\/factors into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.<\/strong><\/p>\n\n\n\n

Autores:<\/strong> Lohre, Rother and Schafer <\/p>\n\n\n\n

T\u00edtulo:<\/strong> Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations <\/p>\n\n\n\n

Did Automated Trading Resurrect the CAPM?<\/a><\/a><\/strong><\/p>\n\n\n\n

Once upon a time, there was everybody\u2019s favourite finance tool in a town \u2013 Capital Asset Pricing Model, which was liked and used by nearly everyone. But a few decades ago, it went out of fashion. Easier accessibility of cheap finance databases allowed a lot of researchers to dig deeper into those data. They uncovered a tremendous amount of evidence for a lot of market anomalies not consistent with CAPM. A new research paper written by Park and Wang shows that CAPM is maybe not completely useless. The rise of automated trading causes individual stocks\u2019 returns to align more closely with the market. Intraday correlation in the equity market is rising, and so is the fraction of firms\u2019 returns that are explained by market returns \u2026 <\/strong><\/p>\n\n\n\n

Autores:<\/strong> Park, Wang<\/p>\n\n\n\n

T\u00edtulo:<\/strong> Did Trading Bots Resurrect the CAPM? <\/p>\n\n\n\n

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Two new strategies have been added.<\/p>\n

Two new related research papers have been included into existing strategy reviews. And two short free blog posts<\/strong><\/a> have been published during last few weeks.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-6429","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/6429","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=6429"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/6429\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=6429"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=6429"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=6429"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}